Author(s): de Souza, Luis A. ; Passos, Leandro A. [UNESP] ; Mendel, Robert ; Ebigbo, Alanna ; Probst, Andreas ; Messmann, Helmut ; Palm, Christoph ; Papa, João P. [UNESP]
Date: 2021
Persistent ID: http://hdl.handle.net/11449/208039
Origin: Oasisbr
Subject(s): Adenocarcinoma; Barrett's esophagus; Generative adversarial networks; Machine learning
Made available in DSpace on 2021-06-25T11:05:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-11-01
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Alexander von Humboldt-Stiftung
Barrett's esophagus figured a swift rise in the number of cases in the past years. Although traditional diagnosis methods offered a vital role in early-stage treatment, they are generally time- and resource-consuming. In this context, computer-aided approaches for automatic diagnosis emerged in the literature since early detection is intrinsically related to remission probabilities. However, they still suffer from drawbacks because of the lack of available data for machine learning purposes, thus implying reduced recognition rates. This work introduces Generative Adversarial Networks to generate high-quality endoscopic images, thereby identifying Barrett's esophagus and adenocarcinoma more precisely. Further, Convolution Neural Networks are used for feature extraction and classification purposes. The proposed approach is validated over two datasets of endoscopic images, with the experiments conducted over the full and patch-split images. The application of Deep Convolutional Generative Adversarial Networks for the data augmentation step and LeNet-5 and AlexNet for the classification step allowed us to validate the proposed methodology over an extensive set of datasets (based on original and augmented sets), reaching results of 90% of accuracy for the patch-based approach and 85% for the image-based approach. Both results are based on augmented datasets and are statistically different from the ones obtained in the original datasets of the same kind. Moreover, the impact of data augmentation was evaluated in the context of image description and classification, and the results obtained using synthetic images outperformed the ones over the original datasets, as well as other recent approaches from the literature. Such results suggest promising insights related to the importance of proper data for the accurate classification concerning computer-assisted Barrett's esophagus and adenocarcinoma detection.
Department of Computing São Carlos Federal University UFSCar
Department of Computing São Paulo State University UNESP
Regensburg Medical Image Computing (ReMIC) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)
Regensburg Center of Health Sciences and Technology (RCHST) Ostbayerische Technische Hochschule Regensburg (OTH Regensburg)
Department of Gastroenterology University Hospital Augsburg
Department of Computing São Paulo State University UNESP
FAPESP: 2013/07375-0
FAPESP: 2014/12236-1
FAPESP: 2017/04847-9
FAPESP: 2019/06533-7
FAPESP: 2019/07665-4
FAPESP: 2019/08605-5
CNPq: 306166/2014-3
CNPq: 307066/2017-7
Alexander von Humboldt-Stiftung: BEX 0581-16-0
